Uniform graph data augmentation may perturb important nodes or edges and then affect the positive correlation of the same object in different views.
Therefore, some guided approaches emerge to identify the importance of nodes or edges through domain knowledge or learnable strategies to keep the semantics inherent in the augmented views.
GCA [1]} proposes adaptive data augmentation, specifically highlighting the important links based on node centrality, and adding noise to unimportant nodes.
MoCL [2]} combines domain knowledge for molecular graph augmentation, without altering the molecular properties too much.
AD-GCL [3]}, which extends the graph information bottleneck [4]} to unsupervised tasks, guides the process of graph data augmentation by a learnable perturbation model for edge removals.
